OpenRaiser/Intern-Atlas huggingface dataset:
Are Minimal Radial Distortion Solvers Really Necessary for Relative Pose Estimation?
Venue: arXiv cs.CV 2025
Year: 2025

Estimating the relative pose between two cameras is a fundamental step in many applications such as Structure-from-Motion. The common approach to relative pose estimation is to apply a minimal solver inside a RANSAC loop. Highly efficient solvers exist for pinhole cameras. Yet, (nearly) all cameras exhibit radial distortion. Not modeling radial distortion leads to (significantly) worse results. However, minimal radial distortion solvers are significantly more complex than pinhole solvers, both in terms of run-time and implementation efforts. This paper compares radial distortion solvers with two simple-to-implement approaches that do not use minimal radial distortion solvers: The first approach combines an efficient pinhole solver with sampled radial undistortion parameters, where the sampled parameters are used for undistortion prior to applying the pinhole solver. The second approach uses a state-of-the-art neural network to estimate the distortion parameters rather than sampling them from a set of potential values. Extensive experiments on multiple datasets, and different camera setups, show that complex minimal radial distortion solvers are not necessary in practice. We discuss under which conditions a simple sampling of radial undistortion parameters is preferable over calibrating cameras using a learning-based prior approach. Code and newly created benchmark for relative pose estimation under radial distortion are available at https://github.com/kocurvik/rdnet.
Balancing Security and Liquidity: A Time-Weighted Snapshot Framework for DAO Governance Voting
Venue: arXiv cs.CR 2025
Year: 2025

As new project upgrading the blockchain industry, novel forms of attack challenges developers to rethink about the design of their innovations. In the growth stage of the development, Decentralized Autonomous Organizations (DAO) introduces different approaches in managing fund through voting in governance tokens. However, relying on tokens as a weight for voting introduces opportunities for hackers to manipulate voting results through flash loan, allowing malicious proposals - fund withdrawal from DAO to hacker's wallet - to execute through the smart contract. In this research, we learned different defense mechanism against the flash loan attack, and their weakness in accessibility that compromise the security of different blockchain projects. Based on our observation, we propose a new defensing structure and apply it with cases.
Drilling into Erasmus learning mobility flows between countries 2014-2024
Venue: arXiv cs.SI 2025
Year: 2025

Analyzing the Erasmus mobility network, we illustrate typical problems and approaches in analyzing weighted networks. We propose alternative exploratory views on the network "Erasmus+ learning mobility flows since 2014". The network has 35 nodes (countries), is very dense, and the range of link weights (number of visits) is huge (from 1 to 217003). An increasing transformation is used to reduce the range. The traditional graph-based visualization is unreadable. To gain insight into the structure of a dense network, it can be reduced to a skeleton by removing less essential links and/or nodes. We have determined the 1-neighbors and 2-neighbors subnetworks. The 1-neighbors skeleton highlights Spain as the main attractor in the network. The 2-neighbors skeleton shows the dominant role of Spain, Germany, France, and Italy. The hubs and authorities, Pathfinder and Ps cores methods confirm these observations. Using the "right" order of the nodes in a matrix representation can reveal the network structure as block patterns in the displayed matrix. The clustering of network nodes based on corrected Salton dissimilarity again shows the dominant role of Spain, Germany, France, and Italy, but also two main clusters of the division into developed/less developed countries. The Balassa normalization (log(measured/expected) visits) matrix shows that most visits within the two main clusters are above expected, while most visits between them are below expected; within the clusters of Balkan countries, Baltic countries, {SK, CZ, HU}, {IS, DK, NO} visits are much above expected, etc.
TunnElQNN: A Hybrid Quantum-classical Neural Network for Efficient Learning
Venue: arXiv cs.LG 2025
Year: 2025

Hybrid quantum-classical neural networks (HQCNNs) represent a promising frontier in machine learning, leveraging the complementary strengths of both models. In this work, we propose the development of TunnElQNN, a non-sequential architecture composed of alternating classical and quantum layers. Within the classical component, we employ the Tunnelling Diode Activation Function (TDAF), inspired by the I-V characteristics of quantum tunnelling. We evaluate the performance of this hybrid model on a synthetic dataset of interleaving half-circle for multi-class classification tasks with varying degrees of class overlap. The model is compared against a baseline hybrid architecture that uses the conventional ReLU activation function (ReLUQNN). Our results show that the TunnElQNN model consistently outperforms the ReLUQNN counterpart. Furthermore, we analyse the decision boundaries generated by TunnElQNN under different levels of class overlap and compare them to those produced by a neural network implementing TDAF within a fully classical architecture. These findings highlight the potential of integrating physics-inspired activation functions with quantum components to enhance the expressiveness and robustness of hybrid quantum-classical machine learning architectures.
Autonomous Embodied Agents: When Robotics Meets Deep Learning Reasoning
Venue: arXiv cs.RO 2025
Year: 2025

The increase in available computing power and the Deep Learning revolution have allowed the exploration of new topics and frontiers in Artificial Intelligence research. A new field called Embodied Artificial Intelligence, which places at the intersection of Computer Vision, Robotics, and Decision Making, has been gaining importance during the last few years, as it aims to foster the development of smart autonomous robots and their deployment in society. The recent availability of large collections of 3D models for photorealistic robotic simulation has allowed faster and safe training of learning-based agents for millions of frames and a careful evaluation of their behavior before deploying the models on real robotic platforms. These intelligent agents are intended to perform a certain task in a possibly unknown environment. To this end, during the training in simulation, the agents learn to perform continuous interactions with the surroundings, such as gathering information from the environment, encoding and extracting useful cues for the task, and performing actions towards the final goal; where every action of the agent influences the interactions. This dissertation follows the complete creation process of embodied agents for indoor environments, from their concept to their implementation and deployment. We aim to contribute to research in Embodied AI and autonomous agents, in order to foster future work in this field. We present a detailed analysis of the procedure behind implementing an intelligent embodied agent, comprehending a thorough description of the current state-of-the-art in literature, technical explanations of the proposed methods, and accurate experimental studies on relevant robotic tasks.
What Makes Teamwork Work? A Multimodal Case Study on Emotions and Diagnostic Expertise in an Intelligent Tutoring System
Venue: arXiv cs.HC 2025
Year: 2025

Teamwork is pivotal in medical teamwork when professionals with diverse skills and emotional states collaborate to make critical decisions. This case study examines the interplay between emotions and professional skills in group decision-making during collaborative medical diagnosis within an Intelligent Tutoring System (ITS). By comparing verbal and physiological data between high-performing and low-performing teams of medical professionals working on a patient case within the ITS, alongside individuals' retrospective collaboration experiences, we employ multimodal data analysis to identify patterns in team emotional climate and their impact on diagnostic efficiency. Specifically, we investigate how emotion-driven dialogue and professional expertise influence both the information-seeking process and the final diagnostic decisions. Grounded in the socially shared regulation of learning framework and utilizing sentiment analysis, we found that social-motivational interactions are key drivers of a positive team emotional climate. Furthermore, through content analysis of dialogue and physiological signals to pinpoint emotional fluctuations, we identify episodes where knowledge exchange and skill acquisition are most likely to occur. Our findings offer valuable insights into optimizing group collaboration in medical contexts by harmonizing emotional dynamics with adaptive strategies for effective decision-making, ultimately enhancing diagnostic accuracy and teamwork effectiveness.
Llama-Nemotron: Efficient Reasoning Models
Venue: arXiv cs.CL 2025
Year: 2025

We introduce the Llama-Nemotron series of models, an open family of heterogeneous reasoning models that deliver exceptional reasoning capabilities, inference efficiency, and an open license for enterprise use. The family comes in three sizes -- Nano (8B), Super (49B), and Ultra (253B) -- and performs competitively with state-of-the-art reasoning models such as DeepSeek-R1 while offering superior inference throughput and memory efficiency. In this report, we discuss the training procedure for these models, which entails using neural architecture search from Llama 3 models for accelerated inference, knowledge distillation, and continued pretraining, followed by a reasoning-focused post-training stage consisting of two main parts: supervised fine-tuning and large scale reinforcement learning. Llama-Nemotron models are the first open-source models to support a dynamic reasoning toggle, allowing users to switch between standard chat and reasoning modes during inference. To further support open research and facilitate model development, we provide the following resources: 1. We release the Llama-Nemotron reasoning models -- LN-Nano, LN-Super, and LN-Ultra -- under the commercially permissive NVIDIA Open Model License Agreement. 2. We release the complete post-training dataset: Llama-Nemotron-Post-Training-Dataset. 3. We also release our training codebases: NeMo, NeMo-Aligner, and Megatron-LM.
Preserving Privacy and Utility in LLM-Based Product Recommendations
Venue: arXiv cs.IR 2025
Year: 2025

Large Language Model (LLM)-based recommendation systems leverage powerful language models to generate personalized suggestions by processing user interactions and preferences. Unlike traditional recommendation systems that rely on structured data and collaborative filtering, LLM-based models process textual and contextual information, often using cloud-based infrastructure. This raises privacy concerns, as user data is transmitted to remote servers, increasing the risk of exposure and reducing control over personal information. To address this, we propose a hybrid privacy-preserving recommendation framework which separates sensitive from nonsensitive data and only shares the latter with the cloud to harness LLM-powered recommendations. To restore lost recommendations related to obfuscated sensitive data, we design a de-obfuscation module that reconstructs sensitive recommendations locally. Experiments on real-world e-commerce datasets show that our framework achieves almost the same recommendation utility with a system which shares all data with an LLM, while preserving privacy to a large extend. Compared to obfuscation-only techniques, our approach improves HR@10 scores and category distribution alignment, offering a better balance between privacy and recommendation quality. Furthermore, our method runs efficiently on consumer-grade hardware, making privacy-aware LLM-based recommendation systems practical for real-world use.
Audio Personas: Augmenting Social Perception via Body-Anchored Audio Cues
Venue: arXiv cs.HC 2025
Year: 2025

We introduce Audio Personas, enabling users to "decorate" themselves with body-anchored sounds in audio augmented reality. Like outfits, makeup, and fragrances, audio personas offer an alternative yet dynamic channel to augment face-to-face interactions. For instance, one can set their audio persona as rain sounds to reflect a bad mood, bee sounds to establish personal boundaries, or a playful "woosh" sound to mimic passing by someone like a breeze. To instantiate the concept, we implemented a headphone-based prototype with multi-user tracking and audio streaming. Our preregistered in-lab study with 64 participants showed that audio personas influenced how participants formed impressions. Individuals with positive audio personas were rated as more socially attractive, more likable, and less threatening than those with negative audio personas. Our study with audio designers revealed that audio personas were preferred in public and semi-public-private spaces for managing social impressions (e.g., personality) and signaling current states (e.g., emotions).
Extended Persistent Homology Distinguishes Simple and Complex Contagions with High Accuracy
Venue: arXiv cs.SI 2025
Year: 2025

The social contagion literature makes a distinction between simple (independent cascade or bond percolation processes that pass infections through edges) and complex contagions (bootstrap percolation or threshold processes that require local reinforcement to spread). However, distinguishing simple and complex contagions using observational data poses a significant challenge in practice. Estimating population-level activation functions from observed contagion dynamics is hindered by confounding factors that influence adoptions (other than neighborhood interactions), as well as heterogeneity in individual behaviors and modeling variations that make it difficult to design appropriate null models for inferring contagion types. Here, we show that a new tool from topological data analysis (TDA), called extended persistent homology (EPH), when applied to contagion processes over networks, can effectively detect simple and complex contagion processes, as well as predict their parameters. We train classification and regression models using EPH-based topological summaries computed on simulated simple and complex contagion dynamics on three real-world network datasets and obtain high predictive performance over a wide range of contagion parameters and under a variety of informational constraints, including uncertainty in model parameters, noise, and partial observability of contagion dynamics. EPH captures the role of cycles of varying lengths in the observed contagion dynamics and offers a useful metric to classify contagion models and predict their parameters. Analyzing geometrical features of network contagion using TDA tools such as EPH can find applications in other network problems such as seeding, vaccination, and quarantine optimization, as well as network inference and reconstruction problems.
The AI Fairness Myth: A Position Paper on Context-Aware Bias
Venue: arXiv cs.CY 2025
Year: 2025

Defining fairness in AI remains a persistent challenge, largely due to its deeply context-dependent nature and the lack of a universal definition. While numerous mathematical formulations of fairness exist, they sometimes conflict with one another and diverge from social, economic, and legal understandings of justice. Traditional quantitative definitions primarily focus on statistical comparisons, but they often fail to simultaneously satisfy multiple fairness constraints. Drawing on philosophical theories (Rawls' Difference Principle and Dworkin's theory of equality) and empirical evidence supporting affirmative action, we argue that fairness sometimes necessitates deliberate, context-aware preferential treatment of historically marginalized groups. Rather than viewing bias solely as a flaw to eliminate, we propose a framework that embraces corrective, intentional biases to promote genuine equality of opportunity. Our approach involves identifying unfairness, recognizing protected groups/individuals, applying corrective strategies, measuring impact, and iterating improvements. By bridging mathematical precision with ethical and contextual considerations, we advocate for an AI fairness paradigm that goes beyond neutrality to actively advance social justice.
SemSpaceFL: A Collaborative Hierarchical Federated Learning Framework for Semantic Communication in 6G LEO Satellites
Venue: arXiv cs.IT 2025
Year: 2025

The advent of the sixth-generation (6G) wireless networks, enhanced by artificial intelligence, promises ubiquitous connectivity through Low Earth Orbit (LEO) satellites. These satellites are capable of collecting vast amounts of geographically diverse and real-time data, which can be immensely valuable for training intelligent models. However, limited inter-satellite communication and data privacy constraints hinder data collection on a single server for training. Therefore, we propose SemSpaceFL, a novel hierarchical federated learning (HFL) framework for LEO satellite networks, with integrated semantic communication capabilities. Our framework introduces a two-tier aggregation architecture where satellite models are first aggregated at regional gateways before final consolidation at a cloud server, which explicitly accounts for satellite mobility patterns and energy constraints. The key innovation lies in our novel aggregation approach, which dynamically adjusts the contribution of each satellite based on its trajectory and association with different gateways, which ensures stable model convergence despite the highly dynamic nature of LEO constellations. To further enhance communication efficiency, we incorporate semantic encoding-decoding techniques trained through the proposed HFL framework, which enables intelligent data compression while maintaining signal integrity. Our experimental results demonstrate that the proposed aggregation strategy achieves superior performance and faster convergence compared to existing benchmarks, while effectively managing the challenges of satellite mobility and energy limitations in dynamic LEO networks.
Real-time Two-tape Control System in Vine robots
Venue: arXiv cs.RO 2025
Year: 2025

This paper focuses on how to make a growing Vine robot steer in different directions with a novel approach to real-time steering control by autonomously applying adhesive tape to induce a surface wrinkles. This enabling real-time directional control with arbitrary many turns while maintaining the robot's soft structure. This system feeds growing material external to the tube. The design achieves fixed-angle turns in 2D space. Through experimental validation, we demonstrate repeated 21-degree turns using a Dubins path planner with minimal error, establishing a foundation for more versatile Vine robot applications. This approach combines real-time control, multi-degree-of-freedom steering, and structural flexibility, addressing key challenges in soft robotics.
Generating Animated Layouts as Structured Text Representations
Venue: arXiv cs.CV 2025
Year: 2025

Despite the remarkable progress in text-to-video models, achieving precise control over text elements and animated graphics remains a significant challenge, especially in applications such as video advertisements. To address this limitation, we introduce Animated Layout Generation, a novel approach to extend static graphic layouts with temporal dynamics. We propose a Structured Text Representation for fine-grained video control through hierarchical visual elements. To demonstrate the effectiveness of our approach, we present VAKER (Video Ad maKER), a text-to-video advertisement generation pipeline that combines a three-stage generation process with Unstructured Text Reasoning for seamless integration with LLMs. VAKER fully automates video advertisement generation by incorporating dynamic layout trajectories for objects and graphics across specific video frames. Through extensive evaluations, we demonstrate that VAKER significantly outperforms existing methods in generating video advertisements. Project Page: https://yeonsangshin.github.io/projects/Vaker
Synthesize-on-Graph: Knowledgeable Synthetic Data Generation for Continue Pre-training of Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Large Language Models (LLMs) have achieved remarkable success but remain data-inefficient, especially when learning from small, specialized corpora with limited and proprietary data. Existing synthetic data generation methods for continue pre-training focus on intra-document content and overlook cross-document knowledge associations, limiting content diversity and depth. We propose Synthetic-on-Graph (SoG), a synthetic data generation framework that incorporates cross-document knowledge associations for efficient corpus expansion. SoG constructs a context graph by extracting entities and concepts from the original corpus, representing cross-document associations, and employing a graph walk strategy for knowledge-associated sampling. This enhances synthetic data diversity and coherence, enabling models to learn complex knowledge structures and handle rare knowledge. To further improve the quality of synthetic data, we integrate two complementary strategies, Chain-of-Thought (CoT) and Contrastive Clarifying (CC), to enhance both reasoning capability and discriminative power. Extensive experiments demonstrate that SoG surpasses state-of-the-art (SOTA) methods on multi-hop and domain-specific question answering, while achieving competitive performance on long-context reading comprehension. These results highlight the superior generalization ability of SoG. Our work advances the paradigm of synthetic data generation and offers practical solutions for efficient knowledge acquisition in LLMs, particularly for downstream tasks and domains with limited training data.
VTS-LLM: Domain-Adaptive LLM Agent for Enhancing Awareness in Vessel Traffic Services through Natural Language
Venue: arXiv cs.CL 2025
Year: 2025

Vessel Traffic Services (VTS) are essential for maritime safety and regulatory compliance through real-time traffic management. However, with increasing traffic complexity and the prevalence of heterogeneous, multimodal data, existing VTS systems face limitations in spatiotemporal reasoning and intuitive human interaction. In this work, we propose VTS-LLM Agent, the first domain-adaptive large LLM agent tailored for interactive decision support in VTS operations. We formalize risk-prone vessel identification as a knowledge-augmented Text-to-SQL task, combining structured vessel databases with external maritime knowledge. To support this, we construct a curated benchmark dataset consisting of a custom schema, domain-specific corpus, and a query-SQL test set in multiple linguistic styles. Our framework incorporates NER-based relational reasoning, agent-based domain knowledge injection, semantic algebra intermediate representation, and query rethink mechanisms to enhance domain grounding and context-aware understanding. Experimental results show that VTS-LLM outperforms both general-purpose and SQL-focused baselines under command-style, operational-style, and formal natural language queries, respectively. Moreover, our analysis provides the first empirical evidence that linguistic style variation introduces systematic performance challenges in Text-to-SQL modeling. This work lays the foundation for natural language interfaces in vessel traffic services and opens new opportunities for proactive, LLM-driven maritime real-time traffic management.